Uncovering inclusivity gaps in design pedagogy through the digital design marginalization framework
Bibliographic record
Abstract
Designers play a key role in the design of inclusive and socially conscious interfaces. Thus, it is imperative for designers to be thoughtful of the ethical and social implications of design. However, gaps in the foundational training that designers receive (e.g., as university students) can negatively impact their ability to consider the social implications of their design practice. This can result in consequences such as digital marginalization, which, as defined by the Digital Design Marginalization (DDM) framework, is the “pushing away”, whether intentional or not, of a defined group of users from a digital or online service or system, where the exclusion has additional, indirect, and long-lasting social consequences on that particular user group. Designers can contribute, even unintentionally, to digital marginalization through their design practices and the design choices they make. We argue that our role as educators includes ensuring not only that our design pedagogy is inclusive, but that the designers we train now are prepared to conduct their future design practice in a manner that is inclusive to all users. As such, we propose to use the Digital Design Marginalization as a lens to guide a reflection-based approach to identify gaps in our pedagogy that may lead to designers becoming ill-equipped to identify how their designs may lead to digital marginalization. Through seven case studies from our own teaching practice, we demonstrate the use of the DDM framework to guide marginalization-focused introspective reflections of curricula. These reflections through the DDM lens revealed gaps in our pedagogy with respect to providing future designers with training that enables them to consider the broader societal and individual implications of the design choices they will make in future practice. Based on our experience using the DDM framework, we then discuss in greater depth how reflection of social consequences of design pedagogy can be operationalized within institutions to reduce educational gaps that may be associated with design-mediated digital marginalization. Finally, we comment on avenues for further development of pedagogical reflection using DDM.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.067 | 0.070 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.008 | 0.003 |
| Science and technology studies | 0.014 | 0.078 |
| Scholarly communication | 0.022 | 0.030 |
| Open science | 0.004 | 0.030 |
| Research integrity | 0.005 | 0.012 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".